Dana Hamerschalg, chief product officer, Miller Heiman Group

Artificial intelligence promises to change the way we work and sell both online and offline. But so far, most sales organizations fall short when it comes to the use of AI. When organizations experiment with various sales enablement technologies, they typically don’t see a strong return on their investment because, thus far, the tools lack the kind of predictive modeling that actually moves the needle on win rates, deal size, time to close, and revenue.

What’s stopping sellers from using AI more effectively? Most companies simply haven’t invested in the right data modeling to use AI engines to link sales actions to better outcomes.

The disconnect between technology and better selling

Across the business-to-business sector of e-commerce, it is much easier today than a few years ago for buyers to shop online for products and complete purchases with little or no involvement from sellers until much later in the process. The bar is high for sellers to demonstrate that they bring a helpful perspective to the table and add value for the buyer. Large purchases with long buying cycles typically have many buying influences involved and these types of sales are perfect opportunities for sellers to distinguish themselves. What sales teams desperately need from AI is a link between insights about the buying situation and sales actions—some guidance around how to actually close the deal.

That’s where sales enablement technology enters the picture. But while sales leaders and managers are giddy about the possibilities with new tools, sales reps are skeptical. The technology can be a double-edged sword. On one hand, it can provide more insight to sales leaders, but on the other it sucks time from sales reps, adding to their administrative burden and actually reducing the time that they would otherwise spend engaging with customers and prospects. Too often, the technology doesn’t generate value for people on the frontlines of B2B sales because it is disconnected from their sales actions and the way they actually engage buyers.

Here’s the problem: Sales enablement technologies usually don’t have a holistic structured data model that links sales actions to sales outcomes.

In many cases, sales enablement tools turn frontline sellers into highly paid data-entry clerks.  With all the best intentions, sales leaders mandate the use of new tools that we think will help our teams, but in reality, they require reps to perform additional tasks—such as data entry, recording themselves practicing pitches, or reviewing practice pitches from peers and grading them—all activity that takes them away from selling. And to add insult to injury, this new data that we have created (by manually telling the technology what good information looks like) still doesn’t link sales actions with sales outcomes. What have we learned that enables us to actually change the outcome of an active deal?

In the current business climate, that’s exactly what sales teams need—insights about specific actions they can take to close business.

Sellers want insights and answers from AI technology

The last thing sellers want is technology that requires them to evaluate mounds of data and draw their own conclusions about what to do next. Instead, they need technology that reduces their administrative burden and provides a short, actionable set of meaningful insights—recommendations for specific sales actions that close deals and demonstrate a strategy for repeatable success.

The key is to link the technology with their sales methodology. And the explosion in sales technology that we’re seeing underscores the need to build and maintain a data schema that links sales actions with outcomes. To be useful, the data must be labeled in a way that it aligns to your sales methodology. Without that, you’re left with exponentially more data points to review, without any greater insight into how to actually improve sales performance on the ground. But with the right data model, the right technology and the right sales methodology, it’s possible for sales teams to convert iffy deals to epic wins and the technology can make the seller’s job a whole lot easier.

What does well-utilized AI look like in a real-world setting? Recently, my company’s CEO, Byron Matthews, co-authored a book on sales enablement. In the chapter on integrated enablement technology, he refers to Gainsight, a customer success management company that uses AI to analyze thousands of metrics, including how long it takes customers to respond to its sales reps. Leveraging aggregated data from the company’s sales interactions, AI guides Gainsight’s sales team toward specific actions that clearly improve customer response times.

By linking sales actions with improved sales results, reps start to view AI technology as a valuable asset—and a tool that is directly tied in to their sales process. Gainsight is a great example of a company that brings this value proposition to life by using AI to generate granular insights about actions sales teams can take to increase wins.

AI adoption hinges on whether or not sales reps directly see value from the organization’s technology investments. If it’s just another tool for management to refine the forecast, it’s not helping the rep much. If you don’t have visibility into how you might change the outcome of your active deals, then you aren’t yet leveraging AI to its full potential. But if you focus on technology that provides insight into what directly drives sales actions, it will guide the way to stronger sales results.

Dana Hamerschlag is chief product officer at Miller Heiman Group, a global sales consultancy. Previously, she was vice president of product management at Ellucian, a provider of software for managing educational institutions, where she led the CRM business, and aconsultant with The Boston Consulting Group, where she led projects for clients across such industries as consumer products, industrial goods, healthcare and technology. Follow her on Twitter @DanaSchlag.